3 years ago
Buenos Aires, ArgentinaSenior
Responsibilities
- Develop, optimize, monitor, and integrate scalable machine learning models using MLOps best practices.
- Implement client requirements from exploratory data analysis and feature engineering through model lifecycle management.
- Build machine learning proof-of-concepts to validate and refine solutions.
- Optimize models for performance, latency, memory usage, and throughput.
- Apply statistical analysis techniques and develop regression models.
- Design and maintain feature stores and data pipelines for machine learning workflows.
- Research and implement emerging machine learning and AI techniques.
- Collaborate with stakeholders to align technical solutions with business needs.
Requirements
- Experience implementing ML-based systems, including model lifecycle management, monitoring, and MLOps pipeline setup.
- Strong proficiency in Python, Pandas, NumPy, Jupyter, Scikit-learn, XGBoost, and Plotly.
- Knowledge of SQL.
- Experience with AWS, GCP, or Azure.
- Preferred experience with Airflow, MLflow, H2O.ai, Databricks, or similar ML workflows.
- Preferred background in modern LLM technologies.
- Preferred understanding of Keras, PyTorch, or TensorFlow.
- Basic knowledge of Docker is preferred.
- Databricks experience with Workflows, Jobs, and Repos is preferred.
Benefits
- Remote-first culture with the ability to work from anywhere.
- In-company English lessons.
- Wellhub or sports club stipend.
- AWS, dbt, Google Cloud, Azure, and Databricks certifications fully covered.
- Food credits through Pedidos Ya.
- Birthday off and an additional vacation week.
- Referral bonuses.
- Annual team trip.
- Monthly childcare reimbursement.
Tech Stack
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